Package: spCP 1.3

Samuel I. Berchuck

spCP: Spatially Varying Change Points

Implements a spatially varying change point model with unique intercepts, slopes, variance intercepts and slopes, and change points at each location. Inference is within the Bayesian setting using Markov chain Monte Carlo (MCMC). The response variable can be modeled as Gaussian (no nugget), probit or Tobit link and the five spatially varying parameter are modeled jointly using a multivariate conditional autoregressive (MCAR) prior. The MCAR is a unique process that allows for a dissimilarity metric to dictate the local spatial dependencies. Full details of the package can be found in the accompanying vignette. Furthermore, the details of the package can be found in the corresponding paper on arXiv by Berchuck et al (2018): "A spatially varying change points model for monitoring glaucoma progression using visual field data", <arxiv:1811.11038>.

Authors:Samuel I. Berchuck [aut, cre]

spCP_1.3.tar.gz
spCP_1.3.tar.gz(r-4.5-noble)spCP_1.3.tar.gz(r-4.4-noble)
spCP_1.3.tgz(r-4.4-emscripten)spCP_1.3.tgz(r-4.3-emscripten)
spCP.pdf |spCP.html
spCP/json (API)

# Install 'spCP' in R:
install.packages('spCP', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblascpp

2.00 score 4 scripts 170 downloads 4 exports 20 dependencies

Last updated 2 years agofrom:b9444cc825. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 04 2024
R-4.5-linux-x86_64OKDec 04 2024

Exports:diagnosticsis.spCPPlotCPspCP

Dependencies:cliexpmfansigenericsgluelatticelifecyclemagrittrMatrixmsmmvtnormpillarpkgconfigRcppRcppArmadillorlangsurvivaltibbleutf8vctrs

Introduction to using R package: spCP

Rendered fromspCP-example.Rmdusingknitr::rmarkdownon Dec 04 2024.

Last update: 2018-11-28
Started: 2018-07-13